Researchers take the degree of feature disentanglement as an important metric to evaluate their methods in computer vision tasks. This is especially true for the image generation task because disentanglement facilitates the control of the attributes of the generated output.
To this end, Chen et al. [1]} first proposed to maximise the mutual information between the attribute classes and the random noise input used to generate samples.
However, due to the random noise still being sampled from the normal distribution like the original GAN[2]}, features cannot be well disentangled because the normal distribution assumptiondistorts the original distribution of the training data.
Later Liu et al. [3]} adopted an Autoencoder model as the backbone and further disentangled the latent vectors of training samples into identity-related and identity-irrelevant categories.
Specifically, the identity-irrelevant branch is implemented by adversarial training, where the branch tries to output the features which have the same identity entropy as the later Softmax classification.
Nonetheless, the visualisation of the features of the identity-irrelevant branch indicated that the extracted features are still relevant to the training samples to some extent.
This causes the distribution of the same semantic meaning features to be disjoint and scattered unevenly in feature space — this makes it difficult to model features of different semantic meanings with known distributions.
After this work, a milestone work, named StyleGAN[4]}, [5]}, provided a feasible disengagement method that improved the benchmark of both image quality and diversity.
The main idea of the work to disentangle features is to map the random noise vectors sampled from the normal distribution \(\mathcal {Z}\)  to an intermediate latent space \(\mathcal {W}\) .
A major benefit of this architecture is that the latent space \(\mathcal {W}\)  does not have to support sampling according to any fixed distribution; its sampling density is induced by a learned piecewise continuous mapping [4]}.
